{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinated Link Sharing on Facebook\n",
    "\n",
    "Malicious actors regularly attempt to manipulate social media platforms using coordinated posting. To date, though, there has been no statistically grounded systemic assessment of the scale and scope of coordinated posting activity -- malicious or otherwise -- on Facebook, the most used social media platform. We construct a list of 16,169 most popular English-language Facebook pages that referenced US politicians in any post, a set of pages that produce more than 91\\% of all user engagement in this category. We collect 11.2 million link posts from these pages between January 20 to April 29, 2021, and use these posts to construct and validate a novel statistically-grounded method for the identification of coordinated link sharing. While past work has focused only on near-simultaneous sharing, something easily disguised, our study includes slower but repetitive sharing missed by previous studies. We find evidence of significant coordinated sharing on Facebook: more than 22.9\\% of Facebook pages engaged in some form of coordinated link posting and 5.5\\% of all links shared by Facebook pages in our data set show evidence of coordination. Large volumes of coordinated sharing occurred between pages publicly managed by the same entity, such as news organizations with a single corporate owner. Yet, there were also many instances of coordination between seemingly independent pages. \n",
    "\n",
    "In this jupyter notebook, we demonstrate how we took the daily edge list (an edge exists between two facebook pages if they share the same link within a day) and did the analyses. We started with a list of 16,169 most popular English-language Facebook pages that referenced US politicians in any post, a set of pages that produce more than 91\\% of all user engagement in this category. Then we collected 11.2 million link posts from these pages between January 20 to April 29, 2021, and use these link posts to construct the edge list.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "from time import time\n",
    "from tqdm import tqdm\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from exp_mixture_model import EMMs\n",
    "from joblib import Parallel, delayed\n",
    "import pickle\n",
    "import plotly.express as px\n",
    "from scipy import stats\n",
    "import scipy\n",
    "\n",
    "import warnings\n",
    "import multiprocessing\n",
    "import scipy.optimize\n",
    "import scipy.stats as st\n",
    "\n",
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read data\n",
    "1. Read daily link co-sharing list and\n",
    "2. Construct a 100 day link sharing network\n",
    "\n",
    "The data consist of source page name and ID, target page name and ID, the url shared, and delta (the time different between share)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100] Days of Data\n"
     ]
    }
   ],
   "source": [
    "#change data folder to your local data repo\n",
    "daily_edge_list = glob.glob(\"/mnt/raid0_24TB/jmcshan/crowdTangle/graphs/2021-*_eng16k_edge_list.txt\")\n",
    "daily_edge_list = sorted(daily_edge_list)\n",
    "daily_edge_list = daily_edge_list[:-4]\n",
    "print(\"[%s] Days of Data\"%len(daily_edge_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:07<00:00, 13.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Link Co-Shared: [880671]\n",
      "                           Source                 Source ID  \\\n",
      "0                 BidenHarris2020  8104823|2851820688439053   \n",
      "1                  USVI Festivals  11379247|478448866894616   \n",
      "2    Pro United Kingdom-Anti E.U.   5651891|760395421259241   \n",
      "3  U.S. Congressman Henry Cuellar   71549|10157659701461551   \n",
      "4      Congresswoman Norma Torres   173347|3942317295793166   \n",
      "\n",
      "                           Target                 Target ID  \\\n",
      "0                  USVI Festivals  11379247|478448866894616   \n",
      "1    Pro United Kingdom-Anti E.U.   5651891|760395421259241   \n",
      "2  U.S. Congressman Henry Cuellar   71549|10157659701461551   \n",
      "3      Congresswoman Norma Torres   173347|3942317295793166   \n",
      "4              The Christian Left     9137|3859590847396357   \n",
      "\n",
      "                                 URL   delta  \n",
      "0  https://bideninaugural.org/watch/  5223.0  \n",
      "1  https://bideninaugural.org/watch/  8285.0  \n",
      "2  https://bideninaugural.org/watch/  1523.0  \n",
      "3  https://bideninaugural.org/watch/   596.0  \n",
      "4  https://bideninaugural.org/watch/   906.0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data_df = pd.DataFrame()\n",
    "for edge_list in tqdm(daily_edge_list):\n",
    "    daily_df = pd.read_csv(edge_list, sep='\\t', header=0, usecols=['Source', 'Source ID', 'Target', 'Target ID', 'diff', 'URL'])\n",
    "    daily_df['delta'] = daily_df['diff']\n",
    "    daily_df = daily_df.drop(['diff'], axis=1)\n",
    "    data_df=data_df.append(daily_df,ignore_index=True)\n",
    "    \n",
    "print(\"Total Link Co-Shared: [%d]\"%len(data_df))\n",
    "print(data_df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pages involved in co-sharing: [11542]\n"
     ]
    }
   ],
   "source": [
    "all_pages = list(data_df.Source.unique()) + list(data_df.Target.unique())\n",
    "total_page = len(set(all_pages))\n",
    "print(\"Pages involved in co-sharing: [%d]\"%total_page)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the number of unique link shared and their avg time by two pages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/raid0_24TB/rpaudel42/miniconda/envs/crowdtangle/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique link shared: [105360]\n",
      "\n",
      "     index                                       Source              Target  \\\n",
      "0     825                                 16 WAPT News                KCCI   \n",
      "1  103503                        WCVB Channel 5 Boston  WTAE-TV Pittsburgh   \n",
      "2  103839  WGAL News Channel 8 Susquehanna Valley, Pa.        WXII 12 NEWS   \n",
      "3  105462                                 WXII 12 NEWS           WLKY News   \n",
      "4  103133                         WBAL-TV 11 Baltimore             WMTW-TV   \n",
      "\n",
      "   mean_inter_share_time  num_of_share  \n",
      "0                    0.0           131  \n",
      "1                    0.0           109  \n",
      "2                    0.0            99  \n",
      "3                    0.0            97  \n",
      "4                    0.0            91  \n"
     ]
    }
   ],
   "source": [
    "edge_count = data_df.groupby(['Source', 'Target'])['delta', 'URL'].agg({'delta':'mean', 'URL':'count'}).reset_index()\n",
    "edge_count.columns = ['Source', 'Target', 'mean_inter_share_time',  'num_of_share']\n",
    "edge_count = edge_count[edge_count['mean_inter_share_time']>=0]\n",
    "\n",
    "#remove self sharing\n",
    "edge_count = edge_count[edge_count['Source'] != edge_count['Target']]\n",
    "\n",
    "edge_count = edge_count.sort_values(['mean_inter_share_time', 'num_of_share'], ascending=(True, False)).reset_index()\n",
    "\n",
    "print(\"Unique link shared: [%s]\"%len(edge_count))\n",
    "print(\"\\n\", edge_count.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Inter-Share time threshold for coordination\n",
    "- Use Exponential-Mixture model to estimate the threshold for identifying coordinated vs organic share\n",
    "\n",
    "- We used the EMMs package in Python to fit a mixture of exponential distributions to our data where we set the range of k between 6 and 10. \n",
    "\n",
    "- Next, we used Decomposed Normalized Maximum-likelihood Codelength (DNML) as a criterion to select the best model. \n",
    "\n",
    "- Last, we identified the threshold where the first component and the second component intersect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EMM_model_fitting(inter_share_data):\n",
    "    # fit EMMs with different values of 'k' (from 6 to 10), i.e., the number of components. \n",
    "    \n",
    "    emms = EMMs(k_candidates=[6, 7, 8, 9, 10])\n",
    "    emms.fit(inter_share_data)\n",
    "    return emms\n",
    "\n",
    "# call EMM_model_fitting() on the new dataset.. \n",
    "# we use the pre-trained model (correspond to Table S4 in our supplementary materials) to demonstrate \n",
    "# as this process is computationally heavy.\n",
    "# emms = EMM_model_fitting(list(edge_count['mean_inter_share_time']))    \n",
    "# print(\"Done EMM\")\n",
    "# with open('emms.pickle', 'wb') as handle:\n",
    "#     pickle.dump(emms, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "with open(\"emm_model/emms3.pickle\", \"rb\") as input_file:\n",
    "    emm= pickle.load(input_file)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'%d' is already calculated.\n",
      "\n",
      "===BEST MODEL====\n",
      "------- EMM(k=8, k_final=5) -------\n",
      "component 1: (pi, mu)=(0.272, 11.909)\n",
      "component 2: (pi, mu)=(0.069, 71.565)\n",
      "component 3: (pi, mu)=(0.018, 156.605)\n",
      "component 4: (pi, mu)=(0.276, 1582.719)\n",
      "component 5: (pi, mu)=(0.365, 21127.994)\n",
      "-----------------------------------\n",
      "None\n",
      "KS-TEST RESULT: \n",
      " KstestResult(statistic=0.3185523800561895, pvalue=0.0)\n",
      "\n",
      "\n",
      "EMM Model RESULT (Correspond to trail 1) : \n",
      "     k_final  marginal_log_likelihood  joint_log_likelihood  AIC  BIC  AIC_LVC  \\\n",
      "k                                                                               \n",
      "6         5            -7.241179e+06         -7.504870e+06  NaN  NaN      NaN   \n",
      "7         5            -7.241136e+06         -7.496253e+06  NaN  NaN      NaN   \n",
      "8         5            -7.241117e+06         -7.459410e+06  NaN  NaN      NaN   \n",
      "9         5            -7.241111e+06         -7.475775e+06  NaN  NaN      NaN   \n",
      "10        5            -7.241175e+06         -7.490624e+06  NaN  NaN      NaN   \n",
      "\n",
      "    BIC_LVC  NML_LVC          DNML  \n",
      "k                                   \n",
      "6       NaN      NaN  7.504941e+06  \n",
      "7       NaN      NaN  7.496323e+06  \n",
      "8       NaN      NaN  7.459479e+06  \n",
      "9       NaN      NaN  7.475845e+06  \n",
      "10      NaN      NaN  7.490694e+06  \n"
     ]
    }
   ],
   "source": [
    "pis = []\n",
    "mus = []\n",
    "pi_holder = []\n",
    "mu_holder = []\n",
    "\n",
    "best_model = emm[1].select('DNML')\n",
    "print(\"\\n===BEST MODEL====\")\n",
    "print(best_model.print_result())\n",
    "print(\"KS-TEST RESULT: \\n\", stats.kstest(list(edge_count['mean_inter_share_time']), best_model.cdf))\n",
    "print(\"\\n\\nEMM Model RESULT (Correspond to trail 1) : \\n\", emm[1].result_table)\n",
    "\n",
    "for j in range(best_model.k_final):\n",
    "    pis.append(best_model.pi[j])\n",
    "    mus.append(best_model.mu[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify the threshold where the first component and the second component intersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     x      exp1      exp2  distance\n",
      "0   20  0.015659  0.010566  0.005093\n",
      "1   21  0.014398  0.010420  0.003978\n",
      "2   22  0.013238  0.010275  0.002963\n",
      "3   23  0.012172  0.010133  0.002039\n",
      "4   24  0.011192  0.009992  0.001200\n",
      "5   25  0.010290  0.009853  0.000437\n",
      "6   26  0.009462  0.009717 -0.000255\n",
      "7   27  0.008700  0.009582 -0.000882\n",
      "8   28  0.007999  0.009449 -0.001450\n",
      "9   29  0.007355  0.009318 -0.001963\n",
      "10  30  0.006762  0.009188 -0.002426\n",
      "11  31  0.006218  0.009061 -0.002843\n",
      "12  32  0.005717  0.008935 -0.003218\n",
      "13  33  0.005256  0.008811 -0.003555\n",
      "14  34  0.004833  0.008689 -0.003856\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KernelDensity\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import expon\n",
    "\n",
    "def fun_px(lmda, x):\n",
    "    return lmda * math.exp(-lmda * x)\n",
    "\n",
    "#Yunkang 5th run\n",
    "lmda1 = mus[0]\n",
    "lmda2 = mus[1]\n",
    "# threshold = 33\n",
    "\n",
    "rows = []\n",
    "for x in range(20, 70):\n",
    "    exp1 = fun_px(1/lmda1, x)\n",
    "    exp2 = fun_px(1/lmda2, x)\n",
    "    rows.append([x, exp1, exp2, exp1-exp2])\n",
    "result_df = pd.DataFrame(rows, columns =['x', 'exp1', 'exp2', 'distance']) \n",
    "print(result_df.head(15))\n",
    "result_df.plot(x=\"x\", y=[\"exp1\", \"exp2\"],  figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, we see that the intersecting point for the two component is 26. We ran the model 5 times and found the average was 27 s and then used it as threshold.\n",
    "\n",
    "We now show the Cumulative Distribution Function (CDF) of time data vs that of the mixture exponential model to see if we fitted the model well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"ecdf_emm.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f09c6156710>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=['Theoretical','Empirical','Row'])\n",
    "sub_df = pd.DataFrame(columns=['Theoretical','Empirical','Row'])\n",
    "\n",
    "dataset = edge_count['mean_inter_share_time']\n",
    "\n",
    "distributions = []\n",
    "for j in range(len(mus)):\n",
    "    distributions.append({\"type\": np.random.exponential, \"kwargs\": {\"scale\": mus[j]}})\n",
    "    \n",
    "coefficients = np.array(pis)\n",
    "coefficients /= coefficients.sum() \n",
    "sample_size = len(dataset)\n",
    "    \n",
    "num_distr = len(distributions)\n",
    "data = np.zeros((sample_size, num_distr))\n",
    "\n",
    "for idx, distr in enumerate(distributions):\n",
    "    data[:, idx] = distr[\"type\"](size=(sample_size,), **distr[\"kwargs\"])\n",
    "random_idx = np.random.choice(np.arange(num_distr), size=(sample_size,), p=coefficients)\n",
    "sample = data[np.arange(sample_size), random_idx]\n",
    "\n",
    "sub_df['Theoretical'] = sample\n",
    "sub_df['Empirical'] = dataset + 0.00001\n",
    "sub_df['Row'] = 0\n",
    "\n",
    "df = pd.concat([df, sub_df])\n",
    "# print(df)\n",
    "fig = px.ecdf(df, log_x = True, facet_row='Row', template=\"simple_white\")\n",
    "\n",
    "fig.update_layout(xaxis_range=[0, 5], yaxis_range=[0,1])\n",
    "fig.update_layout( # customize font and legend orientation & position\n",
    "     legend=dict(\n",
    "        title=None, orientation=\"h\", y=1, yanchor=\"bottom\", x=0.5, xanchor=\"center\"\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.write_html('ecdf_emm.html')\n",
    "IFrame(src='ecdf_emm.html', width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Identify Num of link shared threshold for coordination\n",
    "1. Plot density plot on num of link shared to visualize the number of components\n",
    "2. Use Negative Binomial Mixture Model to estimate the threshold for identifying coordinated vs organic share\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_data = edge_count['num_of_share'].values + 1\n",
    "sns.kdeplot(np.log10(freq_data), log_scale = True,  fill=True)\n",
    "plt.xlabel('number of link shared')\n",
    "plt.xticks([10, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit neg-binomial mixture model in freq data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_like_mix(alpha1, b1, alpha2, b2, w, n):\n",
    "    \"\"\"Log-likeihood of binary Negative Binomial mixture model.\"\"\"\n",
    "    # Fix nonidentifieability be enforcing values of w\n",
    "    if w < 0 or w > 1:\n",
    "        return -np.inf\n",
    "\n",
    "    # Physical bounds on parameters\n",
    "    if alpha1 < 0 or alpha2 < 0 or b1 < 0 or b2 < 0:\n",
    "        return -np.inf\n",
    "\n",
    "    logx1 = st.nbinom.logpmf(n, alpha1, 1/(1+b1))\n",
    "    logx2 = st.nbinom.logpmf(n, alpha2, 1/(1+b2))\n",
    "\n",
    "    # Multipliers for log-sum-exp\n",
    "    lse_coeffs = np.tile([w, 1-w], [len(n), 1]).transpose()\n",
    "\n",
    "    # log-likelihood for each measurement\n",
    "    log_likes = scipy.special.logsumexp(np.vstack([logx1, logx2]), axis=0, b=lse_coeffs)\n",
    "\n",
    "    return np.sum(log_likes)\n",
    "\n",
    "def log_like_iid_nbinom(params, n):\n",
    "    \"\"\"Log likelihood for i.i.d. NBinom measurements, parametrized by alpha, b=1/beta.\"\"\"\n",
    "    alpha, b = params\n",
    "\n",
    "    if alpha <= 0 or b <= 0:\n",
    "        return -np.inf\n",
    "\n",
    "    return np.sum(st.nbinom.logpmf(n, alpha, 1/(1+b)))\n",
    "\n",
    "\n",
    "def mle_iid_nbinom(n):\n",
    "    \"\"\"Perform maximum likelihood estimates for parameters for i.i.d. NBinom measurements, parametrized by alpha, b=1/beta\"\"\"\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "\n",
    "        res = scipy.optimize.minimize(\n",
    "            fun=lambda params, n: -log_like_iid_nbinom(params, n),\n",
    "            x0=np.array([3, 3]),\n",
    "            args=(n,),\n",
    "            method='Powell'\n",
    "        )\n",
    "\n",
    "    if res.success:\n",
    "        return res.x\n",
    "    else:\n",
    "        raise RuntimeError('Convergence failed with message', res.message)\n",
    "        \n",
    "def initial_guess_mix(n, w_guess):\n",
    "    \"\"\"Generate initial guess for mixture model.\"\"\"\n",
    "    n_low = n[n < np.percentile(n, 100*w_guess)]\n",
    "    n_high = n[n >= np.percentile(n, 100*w_guess)]\n",
    "\n",
    "    alpha1, b1 = mle_iid_nbinom(n_low)\n",
    "    alpha2, b2 = mle_iid_nbinom(n_high)\n",
    "\n",
    "    return alpha1, b1, alpha2, b2\n",
    "\n",
    "\n",
    "def mle_mix(n, w_guess):\n",
    "    \"\"\"Obtain MLE estimate for parameters for binary mixture\n",
    "    of Negative Binomials.\"\"\"\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "\n",
    "        res = scipy.optimize.minimize(\n",
    "            fun=lambda params, n: -log_like_mix(*params, n),\n",
    "            x0=[*initial_guess_mix(n, w_guess), w_guess],\n",
    "            args=(n,),\n",
    "            method='Powell',\n",
    "            tol=1e-4,\n",
    "        )\n",
    "\n",
    "    if res.success:\n",
    "        return res.x\n",
    "    else:\n",
    "        raise RuntimeError('Convergence failed with message', res.message)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha1 = 48.440356\n",
      "b1 = 0.027709\n",
      "alpha2 = 0.461302\n",
      "b2 = 134.670241\n",
      "w = 0.904370\n"
     ]
    }
   ],
   "source": [
    "dataset = edge_count['num_of_share'].values\n",
    "\n",
    "# Guess for w \n",
    "w_guess = 0.99\n",
    "\n",
    "popt = mle_mix(dataset, w_guess)\n",
    "alpha1, b1, alpha2, b2, w = popt\n",
    "# Check out optimal parameters\n",
    "print('alpha1 = {0:f}\\nb1 = {1:f}\\nalpha2 = {2:f}\\nb2 = {3:f}\\nw = {4:f}'.format(*popt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify the threshold where the first component and the second component intersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     x  nbinom1  nbinom2  distance\n",
      "0    1    34.75     4.75  0.299966\n",
      "1    2    23.16     3.45  0.197138\n",
      "2    3    10.50     2.81  0.076920\n",
      "3    4     3.64     2.41  0.012289\n",
      "4    5     1.03     2.14 -0.011066\n",
      "5    6     0.25     1.93 -0.016828\n",
      "6    7     0.05     1.77 -0.017165\n",
      "7    8     0.01     1.64 -0.016274\n",
      "8    9     0.00     1.53 -0.015261\n",
      "9   10     0.00     1.43 -0.014346\n",
      "10  11     0.00     1.35 -0.013545\n",
      "11  12     0.00     1.28 -0.012842\n",
      "12  13     0.00     1.22 -0.012219\n",
      "13  14     0.00     1.17 -0.011662\n",
      "14  15     0.00     1.12 -0.011160\n",
      "15  16     0.00     1.07 -0.010705\n",
      "16  17     0.00     1.03 -0.010289\n",
      "17  18     0.00     0.99 -0.009908\n",
      "18  19     0.00     0.96 -0.009556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for x in range(1, 20):\n",
    "    nbinom1 = stats.nbinom.pmf(x, alpha1, 1/(1+b1))\n",
    "    nbinom2 = stats.nbinom.pmf(x, alpha2, 1/(1+b2))\n",
    "#     print(x, \" & \", round(nbinom1*100, 2), \" & \", round(nbinom2*100, 2), \"\\\\\\\\\")\n",
    "    rows.append([x, round(nbinom1*100, 2), round(nbinom2*100, 2), nbinom1-nbinom2])\n",
    "result_df = pd.DataFrame(rows, columns =['x', 'nbinom1', 'nbinom2', 'distance']) \n",
    "print(result_df)\n",
    "result_df.plot(x=\"x\", y=[\"nbinom1\", \"nbinom2\"],  figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intersecting point for the two component is 4. But we used 9 as threshold -- as a more conservative estimate -- because the probability of organically sharing (i.e., the first component) falls to zero at 9 URLs.\n",
    "Hence, final threshold is 9.\n",
    "\n",
    "We now show the Cumulative Distribution Function (CDF) of frequency data vs that of the mixture negative binomial model to see if we fitted the model well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = dataset\n",
    "\n",
    "# Simple ecdf function\n",
    "def ecdf(x):\n",
    "    sx = np.sort(x)\n",
    "    n = sx.size\n",
    "    sy = np.arange(1, n+1)/n\n",
    "    return sx,sy\n",
    "\n",
    "# CDF for theoritical data\n",
    "n_emperical, cdf_emperical = ecdf(n)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.step(n_emperical, cdf_emperical, color='red', label='Empirical',\n",
    "        linewidth=1, zorder=3)\n",
    "\n",
    "n_theor = np.arange(0, n.max()+1)\n",
    "cdf_theor = w * st.nbinom.cdf(n_theor, alpha1, 1/(1+b1))\n",
    "cdf_theor += (1 - w) * st.nbinom.cdf(n_theor, alpha2, 1/(1+b2))\n",
    "\n",
    "ax.step(n_theor, cdf_theor, color='blue', linewidth = 1,\n",
    "        label='Theoretical', zorder = 2)\n",
    "ax.legend(loc='lower right')\n",
    "# ax.set_xlim(-5, 300)\n",
    "ax.set_xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Coordination\n",
    "1. Use the threshold identified above to find different type of coordination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_threshold = 27\n",
    "freq_threshold = 9\n",
    "fast_sharing_pages = []\n",
    "repetitive_sharing_pages = []\n",
    "mix_sharing_pages = []\n",
    "\n",
    "m_count = 0\n",
    "f_count = 0 \n",
    "r_count = 0\n",
    "\n",
    "m_link = 0\n",
    "f_link = 0 \n",
    "r_link = 0\n",
    "\n",
    "m_time = 0\n",
    "f_time = 0 \n",
    "r_time= 0\n",
    "\n",
    "for i, row in edge_count.iterrows():\n",
    "    s_time = row['mean_inter_share_time']\n",
    "    n_url = row['num_of_share']\n",
    "    u = row['Source']\n",
    "    v = row['Target']\n",
    "    if s_time <= time_threshold and n_url >= freq_threshold: #mix (Green)\n",
    "        m_link += n_url\n",
    "        m_count += 1\n",
    "        m_time += s_time\n",
    "        if u not in mix_sharing_pages:\n",
    "            mix_sharing_pages.append(u)\n",
    "        if v not in mix_sharing_pages:\n",
    "            mix_sharing_pages.append(v)\n",
    "    elif n_url >= freq_threshold: # Repetative\n",
    "        r_count += 1\n",
    "        r_link += n_url\n",
    "        r_time += s_time\n",
    "        if u not in repetitive_sharing_pages:\n",
    "            repetitive_sharing_pages.append(u)\n",
    "        if v not in repetitive_sharing_pages:\n",
    "            repetitive_sharing_pages.append(v)\n",
    "    elif s_time <= time_threshold and n_url >= 2: #fast\n",
    "        f_count += 1\n",
    "        f_link += n_url\n",
    "        f_time += s_time\n",
    "        if u not in fast_sharing_pages:\n",
    "            fast_sharing_pages.append(u)\n",
    "        if v not in fast_sharing_pages:\n",
    "            fast_sharing_pages.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type         Pages                Links             Avg. Time      Avg. Link\n",
      "_____________________________________________________________________________________\n",
      "Fast Sharing         658 (4.07\\%)         4793 (0.04\\%)        5.22 sec        3.56\n",
      "Repetitive Sharing   3309 (20.47\\%)       494048 (4.39\\%)      7828.69 sec      82.18\n",
      "Mix Sharing          433 (2.68\\%)         118769 (1.06\\%)      8.60 sec      184.42\n",
      "\n",
      "\n",
      "\n",
      "Total Pages involved in Coordination:  3705 22.91\n",
      "Total links:  617610 5.49\n"
     ]
    }
   ],
   "source": [
    "totalp = 16169\n",
    "totalu = 11248706\n",
    "\n",
    "print(\"Type         Pages                Links             Avg. Time      Avg. Link\")\n",
    "print(\"_____________________________________________________________________________________\") \n",
    "print(\"Fast Sharing        \", len(fast_sharing_pages), \"({:.2f}\\%)\".format((len(fast_sharing_pages)/totalp)*100), \"       \" , f_link, \"({:.2f}\\%)\".format((f_link/totalu)*100), \"      \", \"{:.2f} sec\".format(f_time/f_count), \"      \", \"{:.2f}\".format(f_link/f_count))\n",
    "print(\"Repetitive Sharing  \", len(repetitive_sharing_pages), \"({:.2f}\\%)\".format((len(repetitive_sharing_pages)/totalp)*100), \"     \" , r_link, \"({:.2f}\\%)\".format((r_link/totalu)*100), \"    \", \"{:.2f} sec\".format(r_time/r_count),\"    \", \"{:.2f}\".format(r_link/r_count))\n",
    "print(\"Mix Sharing         \", len(mix_sharing_pages), \"({:.2f}\\%)\".format((len(mix_sharing_pages)/totalp)*100), \"       \" , m_link, \"({:.2f}\\%)\".format((m_link/totalu)*100), \"    \", \"{:.2f} sec\".format(m_time/m_count),\"    \", \"{:.2f}\".format(m_link/m_count))\n",
    "\n",
    "print(\"\\n\\n\")\n",
    "coordination_pages = (mix_sharing_pages + fast_sharing_pages + repetitive_sharing_pages)\n",
    "print(\"Total Pages involved in Coordination: \", len(set(coordination_pages)), \"{:.2f}\".format((len(set(coordination_pages))/totalp)*100))\n",
    "cordinated_link = m_link+f_link+r_link\n",
    "print(\"Total links: \", cordinated_link, \"{:.2f}\".format((cordinated_link/totalu)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import mpld3\n",
    "import math\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "colours = ListedColormap(['#FF0000', '#FF8B11','#FFFF64', '#93C3DD'])  #['#FF0000', '#FF8B11', '#FF8085', '#93C3DD'])#  '#13EAc9'\n",
    "def rand_jitter(arr): \n",
    "    stdev = .015 * (max(arr) - min(arr))\n",
    "    return arr + np.random.randn(len(arr)) * stdev\n",
    "\n",
    "def jitter(x, y, c, s=60, marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs):\n",
    "    return plt.scatter(rand_jitter(x), rand_jitter(y), s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, **kwargs)\n",
    "\n",
    "share_time = []\n",
    "url_count = []\n",
    "dot_label = []\n",
    "dot_colors = []\n",
    "\n",
    "for i, row in edge_count.iterrows():\n",
    "    s_time = row['mean_inter_share_time']\n",
    "    n_url = row['num_of_share']\n",
    "    u = row['Source']\n",
    "    v = row['Target']\n",
    "    if 1 == 1:\n",
    "        share_time.append(math.log10(abs(s_time)+1))\n",
    "        url_count.append(math.log10(n_url))\n",
    "        if s_time <= time_threshold and n_url >= freq_threshold: #mix\n",
    "            dot_colors.append(0) \n",
    "        elif n_url >= freq_threshold: #repetitive\n",
    "            dot_colors.append(2) \n",
    "        elif s_time <= time_threshold and n_url >= 2: #fast\n",
    "            dot_colors.append(1) \n",
    "        else: #norm\n",
    "            dot_colors.append(3)\n",
    "            \n",
    "        \n",
    "classes = ['Fast & Repetitive', 'Fast', 'Repetitive', 'Normal']\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "\n",
    "scatter = jitter(url_count, share_time, c = dot_colors, alpha=0.2, cmap=colours) \n",
    "plt.legend(handles=scatter.legend_elements()[0], labels=classes, fontsize = 15, markerscale=2., frameon=True, loc=1)\n",
    "plt.grid(False)\n",
    "plt.rcParams['axes.facecolor'] = '#D4D4D4'\n",
    "plt.xlabel(\"Num of link shared ($10^x$)\", fontsize = 15)\n",
    "plt.ylabel(\"Inter-share duration in sec ($10^x$)\", fontsize = 15)\n",
    "plt.xticks(fontsize= 15)\n",
    "plt.yticks(fontsize= 15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix = [] \n",
    "fast = []\n",
    "repetitive = []\n",
    "for i, row in edge_count.iterrows():\n",
    "    row_dict = {}\n",
    "    s_time = row['mean_inter_share_time']\n",
    "    n_url = row['num_of_share']\n",
    "    row_dict['Source'] = row['Source']\n",
    "    row_dict['Target'] = row['Target']\n",
    "    row_dict['mean_inter_share_time'] = round(row['mean_inter_share_time'], 2)\n",
    "    row_dict['num_of_share'] = row['num_of_share']\n",
    "    if s_time <= time_threshold and n_url >= freq_threshold: #mix\n",
    "        mix.append(row_dict)\n",
    "    elif n_url >= freq_threshold: #repetitive\n",
    "        repetitive.append(row_dict)\n",
    "    elif s_time <= time_threshold and n_url >= 2: #fast\n",
    "        fast.append(row_dict)\n",
    "    else: #norm\n",
    "        pass\n",
    "fast_df = pd.DataFrame(fast)\n",
    "repetitive_df = pd.DataFrame(repetitive)\n",
    "mix_df = pd.DataFrame(mix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Collect all coordinated sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_df['type'] = 'Fast'\n",
    "mix_df['type'] = 'Mixed'\n",
    "repetitive_df['type'] = 'Repetitive'\n",
    "cord_df = mix_df.append(fast_df, ignore_index=True)\n",
    "cord_df = cord_df.append(repetitive_df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Nodes: 629\n",
      "Nodes : 629  Edges : 2000\n"
     ]
    }
   ],
   "source": [
    "page_color_list = ['#FF0000', '#FF8B11','#FFFF64', '#93C3DD']\n",
    "\n",
    "def get_node_map(data_df):\n",
    "    node_map = {}\n",
    "    node_id = 0\n",
    "    for index, row in data_df.iterrows():\n",
    "        src = row.Source\n",
    "        dst = row.Target\n",
    "        if src not in node_map:\n",
    "            node_map[src] = node_id\n",
    "            node_id += 1\n",
    "        if dst not in node_map:\n",
    "            node_map[dst] = node_id\n",
    "            node_id += 1\n",
    "    return node_map\n",
    "\n",
    "def get_edge_color(edges):\n",
    "    edge_color = {}\n",
    "    for i, row in edges.iterrows():\n",
    "        key = (row['Source'], row['Target'])\n",
    "        s_time = row['mean_inter_share_time']\n",
    "        n_url = row['num_of_share']\n",
    "        if s_time <= time_threshold and n_url >= freq_threshold: #mix\n",
    "            edge_color[key] = '#FF0000' \n",
    "        elif n_url >= freq_threshold: #repetitive\n",
    "            edge_color[key] = '#FFFF64' \n",
    "        elif s_time <= time_threshold and n_url >= 2: #fast\n",
    "            edge_color[key] = '#FF8B11' \n",
    "        else: #norm\n",
    "            edge_color[key] = '#93C3DD' \n",
    "    return edge_color\n",
    "        \n",
    "def get_page_color(node_map):\n",
    "    page_color = {}\n",
    "    for k, v in node_map.items():\n",
    "        if k in mix_sharing_pages: \n",
    "            page_color[k] = '#FF0000' \n",
    "        elif k in repetitive_sharing_pages: #\n",
    "            page_color[k] = '#FFFF64' \n",
    "        elif k in fast_sharing_pages: \n",
    "            page_color[k] = '#FF8B11' \n",
    "        else: #gray\n",
    "            page_color[k] = '#93C3DD' \n",
    "\n",
    "    return page_color\n",
    "\n",
    "def create_graph_df(df, node_map):\n",
    "    G = nx.MultiGraph() \n",
    "    #nodes \n",
    "    for index, row in df.iterrows():\n",
    "        src = row.Source\n",
    "        target = row.Target\n",
    "        G.add_node(src, name= src)\n",
    "        G.add_node(target, name = target)\n",
    "        s_time = row['mean_inter_share_time']\n",
    "        n_url = row['num_of_share']\n",
    "        color = '#93C3DD'\n",
    "        if s_time <= time_threshold and n_url >= freq_threshold: #mix\n",
    "            color = '#FF0000'\n",
    "        elif n_url >= freq_threshold: #repetitive\n",
    "            color = '#FFFF64'\n",
    "        elif s_time <= time_threshold and n_url >= 2: #fast\n",
    "            color = '#FF8B11'\n",
    "        if color == '#93C3DD':\n",
    "            G.add_edge(src, target, edge_color = color, weight=row.num_of_share, time = row.mean_inter_share_time, edge_alpha=0.3)\n",
    "        else:\n",
    "            G.add_edge(src, target, edge_color = color, weight=row.num_of_share, time = row.mean_inter_share_time, edge_alpha=1)\n",
    "    print(\"Nodes :\", len(G.nodes()), \" Edges :\", len(G.edges()))\n",
    "    return G\n",
    "\n",
    "\n",
    "\n",
    "# use df_cur = edge_count to generate Fig 1(b)\n",
    "df_cur = edge_count[:2000]\n",
    "\n",
    "node_map = get_node_map(df_cur)\n",
    "node_list = [str(v) for k, v in node_map.items()]\n",
    "print(\"Total Nodes: %s\"%len(node_list))\n",
    "G = create_graph_df(df_cur, node_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200px\"\n",
       "            height=\"800px\"\n",
       "            src=\"test.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f09bb47b910>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Network(height=\"800px\", width=\"1200px\", notebook=True, directed=True, heading=\"Facebook Network\")\n",
    "net.force_atlas_2based(gravity=-50, central_gravity=0.01, spring_length=100, spring_strength=0.08, damping=0.4, overlap=0)\n",
    "\n",
    "for n, data in G.nodes(data=True):\n",
    "    net.add_node(n, shape='dot', size=10, color='#93C3DD', borderWidth = 1)\n",
    "for u, v, data in G.edges(data=True):\n",
    "    net.add_edge(u, v, value=0.0001*data['weight'], color= data['edge_color'], physics=True, alpha=data['edge_alpha'])\n",
    "net.set_edge_smooth('dynamic')\n",
    "# net.save_graph('test_2url.html')\n",
    "net.show('test.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find 10 largest networks involved in different type of sharing\n",
    "\n",
    "Table S10-12 in Supplementary material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes : 658  Edges : 1347\n",
      "1. RGJ  Reno Gazette Journal, PackersNews, Lansing State Journal, TCPalm, New Jersey Herald, The News-Star, lohud, The Columbus Dispatch, The Daily News Journal - dnj.com, Newark Advocate, The Enterprise of Brockton, Enquirer - Cincinnati and Kentucky, The Post-Crescent (Appleton-Fox Cities, Wisconsin), Times Herald newspaper in Port Huron, Michigan, Houma Today, The Star Press, Taunton Daily Gazette, The Augusta Chronicle, Burlington Free Press, SouthCoast Today, Tennessean, Public Opinion, San Angelo Standard-Times, Pacific Daily News, Courier & Press, Springfield News-Leader, Monroe News, Asheville Citizen Times, The Daily Telegram - Adrian, Mich., The Hornell Evening Tribune, Kitsap Sun, Delmarva Now - Daily Times, The Commercial Appeal, The Tuscaloosa News, Montgomery Advertiser, StarNews, Times Record News, The Hutchinson News, The Jackson Sun, The Gaston Gazette, IndyStar, Akron Beacon Journal, Abilene Reporter-News, Foster's Daily Democrat, Blueridgenow, Naples Daily News, Democrat and Chronicle, Mansfield News Journal, pressconnects.com / Press & Sun-Bulletin, The Daily Record, Wooster, OH, Corpus Christi Caller-Times, ithacajournal.com / Ithaca Journal, Argus Leader Media, The Newport Daily News, The Times-Reporter, The Shreveport Times, Green Bay Press-Gazette, The News-Press (Fort Myers and Cape Coral), The Coloradoan, Observer-Dispatch, Zanesville Times Recorder, The Sheboygan Press, The Courier-Tribune, Salina Journal, The News Leader, Knoxville News Sentinel, FdL Reporter, Desert Sun, NorthJersey.com, Delaware Online, Pensacola News Journal, Savannah Morning News & SavannahNow.com, Tidesports.com, The Fayetteville Observer, Florida Today, Tallahassee Democrat, Poughkeepsie Journal, Clarion Ledger, Manitowoc Herald Times Reporter, The Livingston Daily Press & Argus, Journal & Courier, Lebanon Daily News, Seacoastonline.com, The Leaf-Chronicle - TheLeafChronicle.com, York Daily Record/Sunday News, The Asbury Park Press, Pocono Record, Athens Banner-Herald & OnlineAthens.com, The Shelby Star, The Greenville News, The Pueblo Chieftain, Spartanburg Herald-Journal, The Garden City Telegram, Battle Creek Enquirer Total nodes: 94 \n",
      "Total Edges: 183 \n",
      "Link Shared: 605 \n",
      "Avg. Time: 2.53 sec\n",
      "\n",
      "\n",
      "2. WFLX FOX 29, KLTV 7, KSWO7News, WBTV News, WAFB Channel 9, KCBD NewsChannel 11, NewsChannel10, WLOX-TV, WSFA-TV, KPLC 7 News, 14 NEWS, WMC Action News 5, KOLD News 13, WTVM News Leader 9, Live 5 News, WBRC FOX6 News, KSLA News 12, WDAM TV, KFVS-TV, News Channel 6 KAUZ, WTOC-TV, WALB News 10, WAFF 48 News, FOX19, Region 8 News, ABC7 Sarasota - WWSB, WVUE FOX 8 News, KTRE-TV, WMBF News, WIS TV, WECT News, WLBT 3 On Your Side Total nodes: 32 \n",
      "Total Edges: 303 \n",
      "Link Shared: 1020 \n",
      "Avg. Time: 0.21 sec\n",
      "\n",
      "\n",
      "3. Classic Rock 101.1, Majic 95.5, The Bull, Seattle Wolf, Alice 105.9, ALT 105.3, 98.7 Simon, Froggy 101, 100.1 FM and AM 1020 KDKA, K-FROG, 99.5 WYCD, Y108, US99 / Chicago's Hottest Country, ALT 949, KyXy Radio 96.5 FM, Bayou 95.7, 98.7 KLUV, 92.5 WBEE, 100.7 Star, Mix 94.7, 98.5 KRZ, Kiss 98.5, 103.7 KSON, KS1075, Star 102.5, 93.7 The Fan, 95-7 R&B, MAGIC 106.7, WCBS-FM 101.1 - New York's Greatest Hits Total nodes: 29 \n",
      "Total Edges: 37 \n",
      "Link Shared: 91 \n",
      "Avg. Time: 1.21 sec\n",
      "\n",
      "\n",
      "4. WESH 2 News, WMUR-TV, WDSU News, WJCL News, WTAE-TV Pittsburgh, WVTM 13, WLKY News, WGAL News Channel 8 Susquehanna Valley, Pa., KCCI, 16 WAPT News, WBAL-TV 11 Baltimore, WYFF News 4, KMBC 9, WMTW-TV, KSBW TV Action News 8, WBAL NewsRadio 1090 and FM 101.5, WCVB Channel 5 Boston, KOAT, WPBF 25 News, KCRA 3, My NBC5, 40/29 News -- Fort Smith & Fayetteville, Arkansas, WISN 12 NEWS, KETV NewsWatch 7, KOCO 5 News, WXII 12 NEWS, WLWT Total nodes: 27 \n",
      "Total Edges: 212 \n",
      "Link Shared: 830 \n",
      "Avg. Time: 0.29 sec\n",
      "\n",
      "\n",
      "5. Nets Nation, Buccaneers Nation, NBA on ClutchPoints, Packers Nation, Warriors Nation, Ravens Nation, Celtics Nation, Raptors Nation, Sixers Nation, Patriots Nation, Knicks Nation, Bucks Nation, Spurs Nation, Nuggets Nation, Steelers Nation, Grizzlies Nation, LeBron Nation, Chiefs Kingdom, LakeShow, Mavs Nation, Hawks Nation, Raider Nation, Pelicans Nation, Blazers Nation, NFL on ClutchPoints Total nodes: 25 \n",
      "Total Edges: 141 \n",
      "Link Shared: 531 \n",
      "Avg. Time: 10.23 sec\n",
      "\n",
      "\n",
      "6. Houston, TX - Lost Dogs, Cats & Pets, Tucson, AZ - Lost Dogs, Cats & Pets, Oklahoma City - Lost Dogs, Cats & Pets, Memphis, TN - Lost Dogs, Cats & Pets, Modesto, CA - Lost Dogs, Cats & Pets, Corpus Christi, TX - Lost Dogs, Cats & Pets, Sacramento, CA - Lost Dogs, Cats & Pets, Raleigh, NC - Lost Dogs, Cats & Pets, Minneapolis, MN - Lost Dogs, Cats & Pets, Las Vegas - Lost Dogs, Cats & Pets, Indianapolis, Indiana - Lost Dogs, Cats & Pets, Los Angeles, CA - Lost Dogs, Cats & Pets, Miami, FL - Lost Dogs, Cats & Pets, Bakersfield, CA - Lost Dogs, Cats & Pets, Little Rock, AR - Lost Dogs, Cats & Pets, Austin, TX - Lost Dogs, Cats & Pets Total nodes: 16 \n",
      "Total Edges: 15 \n",
      "Link Shared: 114 \n",
      "Avg. Time: 3.26 sec\n",
      "\n",
      "\n",
      "7. Philadelphia Eagles on CBS Sports, Minnesota Vikings on CBS Sports, Washington Football Team on CBS Sports, Miami Dolphins on CBS Sports, Atlanta Falcons on CBS Sports, New York Giants on CBS Sports, Cincinnati Bengals on CBS Sports, Green Bay Packers on CBS Sports, Tampa Bay Buccaneers on CBS Sports, Carolina Panthers on CBS Sports, Jacksonville Jaguars on CBS Sports, New England Patriots on CBS Sports, Pittsburgh Steelers on CBS Sports, New York Jets on CBS Sports, Dallas Cowboys on CBS Sports Total nodes: 15 \n",
      "Total Edges: 33 \n",
      "Link Shared: 103 \n",
      "Avg. Time: 12.36 sec\n",
      "\n",
      "\n",
      "8. Wisconsin Firearms Coalition, Pennsylvania Firearms Association, Ohio Gun Owners, Washington Gun Rights, Idaho Second Amendment Alliance, New York State Firearms Association, North Carolina Firearms Coalition, Minnesota Gun Rights, Missouri Firearms Coalition, WyGO / Wyoming Gun Owners, Georgia Gun Owners, Inc., American Firearms Association, Iowa Gun Owners Total nodes: 13 \n",
      "Total Edges: 45 \n",
      "Link Shared: 153 \n",
      "Avg. Time: 18.19 sec\n",
      "\n",
      "\n",
      "9. Wild Edible and Medicinal Plants, Forest Schooling, Nature Learning, Earthschooling, Living Off-Grid, Being Green, Permaculture, Everything Animals, Everything Gardening, Canning, Preserving and Dehydrating Food, Sustainable Living, Everything Wild, Growing Organic, Eating Organic, Rewilding Total nodes: 12 \n",
      "Total Edges: 13 \n",
      "Link Shared: 43 \n",
      "Avg. Time: 21.56 sec\n",
      "\n",
      "\n",
      "10. BuzzFeed, BuzzFeed News, Watch As/Is, BuzzFeed LGBTQ, BuzzFeed BFF, BuzzFeed Entertainment, BuzzFeed UK, BuzzFeed Animals, Cocoa Butter, SOML, BuzzFeed Celeb Total nodes: 11 \n",
      "Total Edges: 10 \n",
      "Link Shared: 29 \n",
      "Avg. Time: 5.51 sec\n",
      "\n",
      "\n",
      "11. Hits Radio, Northsound 1, Pulse 1, Hallam FM, Free Radio, Metro Radio, Tay FM, Forth 1, Clyde 1, Pirate FM Total nodes: 10 \n",
      "Total Edges: 44 \n",
      "Link Shared: 145 \n",
      "Avg. Time: 1.03 sec\n",
      "\n",
      "\n",
      "12. Minnesota - Lost Dogs, Cats & Pets, Fayetteville, NC - Lost Dogs, Cats & Pets, Indiana - Lost Dogs, Cats & Pets, Texas - Lost Dogs, Cats & Pets, Iowa - Lost Dogs, Cats & Pets, South Carolina - Lost Dogs, Cats & Pets, Louisiana - Lost Dogs, Cats & Pets, Georgia - Lost Dogs, Cats & Pets, North Carolina - Lost Dogs, Cats & Pets Total nodes: 9 \n",
      "Total Edges: 8 \n",
      "Link Shared: 64 \n",
      "Avg. Time: 7.83 sec\n",
      "\n",
      "\n",
      "13. Dearly, Inappropriate & Unfiltered Momma, Nameless Network, Journeys Of A Woman, I can insult my best friend, but if you do you're going down., Nameless Network Presents, I Am Fed Up With Your Lies And Cheating, Proud Moms Total nodes: 8 \n",
      "Total Edges: 12 \n",
      "Link Shared: 38 \n",
      "Avg. Time: 8.18 sec\n",
      "\n",
      "\n",
      "14. Black Skip Bayless, Houston Texans Houston Rockets Everything, Crying Jordan, Total Pro Sports, Sports Memes, Dallas Cowboys and Their Fans are Morons, XFL Memes, Black Adam Schefter Total nodes: 8 \n",
      "Total Edges: 9 \n",
      "Link Shared: 38 \n",
      "Avg. Time: 15.10 sec\n",
      "\n",
      "\n",
      "15. The Country Daily, New Country 96.3, WBAP, B98.7, 95sx Hit Music Now, WMAL DC, 99.5 The Wolf Total nodes: 7 \n",
      "Total Edges: 22 \n",
      "Link Shared: 48 \n",
      "Avg. Time: 1.92 sec\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# df_cur = repetitive_df\n",
    "df_cur = fast_df\n",
    "# df_cur = mix_df\n",
    "\n",
    "node_map = get_node_map(df_cur)\n",
    "G = create_graph_df(df_cur, node_map)\n",
    "\n",
    "networks = [G.subgraph(c).copy() for c in sorted(nx.connected_components(G), key=len, reverse=True)]\n",
    "i = 1 \n",
    "for idx, net in enumerate(networks[:15]):\n",
    "   \n",
    "    total_edge = len(net.edges())\n",
    "    stats = []\n",
    "    stats.append(\"Total nodes: \" + str(len(net.nodes())))\n",
    "    stats.append(\"Total Edges: \" + str(total_edge))\n",
    "    stats.append(\"Link Shared: \" + str(sum([data['weight'] for u, v, data in net.edges(data=True)])))\n",
    "    stats.append(\"Avg. Time: \" + str(\"{:.2f} sec\".format(sum([data['time'] for u, v, data in net.edges(data=True)])/total_edge)))\n",
    "\n",
    "    pages = [data['name'] for n, data in net.nodes(data=True)]\n",
    "    print(str(i)+\".\", ', '.join(pages), ' \\n'.join(stats))\n",
    "    print('\\n')\n",
    "    i += 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find domain used in coordination\n",
    "\n",
    "- Find coordinated links with url\n",
    "- Extract domain name from url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BidenHarris2020</td>\n",
       "      <td>USVI Festivals</td>\n",
       "      <td>https://bideninaugural.org/watch/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USVI Festivals</td>\n",
       "      <td>Pro United Kingdom-Anti E.U.</td>\n",
       "      <td>https://bideninaugural.org/watch/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pro United Kingdom-Anti E.U.</td>\n",
       "      <td>U.S. Congressman Henry Cuellar</td>\n",
       "      <td>https://bideninaugural.org/watch/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U.S. Congressman Henry Cuellar</td>\n",
       "      <td>Congresswoman Norma Torres</td>\n",
       "      <td>https://bideninaugural.org/watch/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Congresswoman Norma Torres</td>\n",
       "      <td>The Christian Left</td>\n",
       "      <td>https://bideninaugural.org/watch/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Source                          Target  \\\n",
       "0                 BidenHarris2020                  USVI Festivals   \n",
       "1                  USVI Festivals    Pro United Kingdom-Anti E.U.   \n",
       "2    Pro United Kingdom-Anti E.U.  U.S. Congressman Henry Cuellar   \n",
       "3  U.S. Congressman Henry Cuellar      Congresswoman Norma Torres   \n",
       "4      Congresswoman Norma Torres              The Christian Left   \n",
       "\n",
       "                                 URL  \n",
       "0  https://bideninaugural.org/watch/  \n",
       "1  https://bideninaugural.org/watch/  \n",
       "2  https://bideninaugural.org/watch/  \n",
       "3  https://bideninaugural.org/watch/  \n",
       "4  https://bideninaugural.org/watch/  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subset = data_df.iloc [:, [0, 2, 4]] \n",
    "data_subset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix_pages = mix_df.loc[:, ['Source', 'Target']]\n",
    "mix_with_url = pd.merge(mix_pages, data_subset,  how='left', left_on=['Source','Target'], right_on = ['Source','Target'])\n",
    "# mix_with_url = mix_with_url.sort_values(by='mean_share_time')\n",
    "mix_with_url['type'] = 'Mixed'\n",
    "\n",
    "fast_pages = fast_df.loc[:, ['Source', 'Target']]\n",
    "fast_with_url = pd.merge(fast_pages, data_subset,  how='left', left_on=['Source','Target'], right_on = ['Source','Target'])\n",
    "# fast_with_url = fast_with_url.sort_values(by='mean_share_time')\n",
    "fast_with_url['type'] = 'Fast'\n",
    "\n",
    "repetitive_pages = repetitive_df.loc[:, ['Source', 'Target']]\n",
    "repetitive_with_url = pd.merge(repetitive_pages, data_subset,  how='left', left_on=['Source','Target'], right_on = ['Source','Target'])\n",
    "# repetitive_with_url = repetitive_with_url.sort_values(by='mean_share_time')\n",
    "repetitive_with_url['type'] = 'Repetative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Source          Target  \\\n",
      "0              16 WAPT News            KCCI   \n",
      "1              16 WAPT News            KCCI   \n",
      "2              16 WAPT News            KCCI   \n",
      "3              16 WAPT News            KCCI   \n",
      "4              16 WAPT News            KCCI   \n",
      "...                     ...             ...   \n",
      "617605  The Firearm Project  Police Tribune   \n",
      "617606  The Firearm Project  Police Tribune   \n",
      "617607  The Firearm Project  Police Tribune   \n",
      "617608  The Firearm Project  Police Tribune   \n",
      "617609  The Firearm Project  Police Tribune   \n",
      "\n",
      "                                                      URL        type  \n",
      "0       https://www.wcvb.com/article/tail-waggin-reuni...       Mixed  \n",
      "1       https://www.wesh.com/article/loud-boom-florida...       Mixed  \n",
      "2       https://www.mynbc5.com/article/meet-the-vermon...       Mixed  \n",
      "3       https://www.wyff4.com/article/coyote-makes-its...       Mixed  \n",
      "4       https://www.wcvb.com/article/noise-complaint-l...       Mixed  \n",
      "...                                                   ...         ...  \n",
      "617605  https://policetribune.com/capitol-police-equip...  Repetative  \n",
      "617606  https://policetribune.com/court-says-deputy-kn...  Repetative  \n",
      "617607  https://policetribune.com/hero-down-douglas-co...  Repetative  \n",
      "617608  https://policetribune.com/signs-at-minneapolis...  Repetative  \n",
      "617609  https://policetribune.com/hero-down-olmstead-c...  Repetative  \n",
      "\n",
      "[617610 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "df_coordination = mix_with_url.append(fast_with_url, ignore_index=True)\n",
    "#print(\"Total Edges: %s\"%len(df_cur))\n",
    "df_coordination = df_coordination.append(repetitive_with_url, ignore_index=True)\n",
    "print(df_coordination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract domain name from URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tldextract import extract\n",
    "\n",
    "def get_domain_name(url):\n",
    "    tsd, td, tsu = extract(url) \n",
    "    domain_name = td + '.' + tsu \n",
    "    return(domain_name)\n",
    "\n",
    "df_coordination['domain_name'] = df_coordination['URL'].apply(get_domain_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total domain in engage in coordinations [3264]\n"
     ]
    }
   ],
   "source": [
    "print(\"Total domain in engage in coordinations [%d]\"%len(df_coordination['domain_name'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['domain_name'] = data_df['URL'].apply(get_domain_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total domain in our dataset [7449]\n"
     ]
    }
   ],
   "source": [
    "print(\"Total domain in our dataset [%d]\"%len(data_df['domain_name'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 400 domains \n",
    "\n",
    "Table S9 in supplementary materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain_name</th>\n",
       "      <th>num_of_link_shared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3047</th>\n",
       "      <td>wegotthiscovered.com</td>\n",
       "      <td>38647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7news.com.au</td>\n",
       "      <td>24566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>legit.ng</td>\n",
       "      <td>22387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>adomonline.com</td>\n",
       "      <td>16469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2632</th>\n",
       "      <td>theepochtimes.com</td>\n",
       "      <td>12510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>augustachronicle.com</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>tvline.com</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>perezhilton.com</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>magiccityweekend.com</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>soaphub.com</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               domain_name  num_of_link_shared\n",
       "3047  wegotthiscovered.com               38647\n",
       "30            7news.com.au               24566\n",
       "1534              legit.ng               22387\n",
       "91          adomonline.com               16469\n",
       "2632     theepochtimes.com               12510\n",
       "...                    ...                 ...\n",
       "221   augustachronicle.com                 190\n",
       "2875            tvline.com                 190\n",
       "2062       perezhilton.com                 188\n",
       "1605  magiccityweekend.com                 188\n",
       "2411           soaphub.com                 187\n",
       "\n",
       "[400 rows x 2 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cordinated_domain = df_coordination.groupby(['domain_name']).agg({'domain_name':['count']}).reset_index()\n",
    "cordinated_domain.columns = ['domain_name', 'num_of_link_shared']\n",
    "cordinated_domain =  cordinated_domain.sort_values(by='num_of_link_shared', ascending=False)\n",
    "cordinated_domain[:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_domain = df_coordination.iloc[:,[3,4]]\n",
    "type_domain = df_coordination.groupby(['type','domain_name']).agg({'domain_name':['nunique']}).reset_index()\n",
    "type_domain.columns = ['type','domain_name', 'count']\n",
    "type_domain = type_domain.groupby(['type']).agg({'domain_name':['count']}).reset_index()\n",
    "\n",
    "df_coordination.groupby(['type','domain_name']).agg({'domain_name':['nunique']}).reset_index()\n",
    "coordination_type_vs_domain = df_coordination.groupby(['type']).agg({'domain_name':['count']}).reset_index()\n",
    "coordination_type_vs_domain.columns = ['type','num_of_share']\n",
    "coordination_type_vs_domain  = coordination_type_vs_domain.sort_values(by='num_of_share', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find domain black listed by Media Bias Fact Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbfc_clean = pd.read_csv(\"MBFC_clean.csv\")\n",
    "mbfc_domain = list(mbfc_clean.domain.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbfc_domain_cat = dict(zip(mbfc_clean.domain, mbfc_clean.MBFC_category))\n",
    "# print(mbfc_domain_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_mbfc(domain):\n",
    "    if domain in mbfc_domain_cat:\n",
    "        return mbfc_domain_cat[domain]\n",
    "    else:\n",
    "        return 'Not listed'\n",
    "df_coordination['MBFC_category'] = df_coordination['domain_name'].apply(check_mbfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Questional and Consipracy links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Source                    Target  \\\n",
      "1613             Gosnell Movie  The Ann and Phelim Scoop   \n",
      "1618             Gosnell Movie  The Ann and Phelim Scoop   \n",
      "3621    I Support Donald Trump         Headline Politics   \n",
      "3622    I Support Donald Trump         Headline Politics   \n",
      "3623    I Support Donald Trump         Headline Politics   \n",
      "...                        ...                       ...   \n",
      "617059                PJ Media                  RedState   \n",
      "617060                PJ Media                  RedState   \n",
      "617061                PJ Media                  RedState   \n",
      "617062                PJ Media                  RedState   \n",
      "617063                PJ Media                  RedState   \n",
      "\n",
      "                                                      URL        type  \\\n",
      "1613    https://www.liveaction.org/news/sanctity-human...       Mixed   \n",
      "1618    https://www.washingtontimes.com/news/2021/mar/...       Mixed   \n",
      "3621    https://thepoliticalinsider.com/seattle-defund...       Mixed   \n",
      "3622    https://thepoliticalinsider.com/report-trump-w...       Mixed   \n",
      "3623    https://thepoliticalinsider.com/cnn-journalist...       Mixed   \n",
      "...                                                   ...         ...   \n",
      "617059  https://redstate.com/nick-arama/2021/04/25/man...  Repetative   \n",
      "617060  https://redstate.com/sister-toldjah/2021/04/26...  Repetative   \n",
      "617061  https://redstate.com/sister-toldjah/2021/04/26...  Repetative   \n",
      "617062  https://redstate.com/alexparker/2021/04/26/tra...  Repetative   \n",
      "617063  https://redstate.com/sister-toldjah/2021/04/28...  Repetative   \n",
      "\n",
      "                    domain_name MBFC_category  \n",
      "1613             liveaction.org    Conspiracy  \n",
      "1618        washingtontimes.com  Questionable  \n",
      "3621    thepoliticalinsider.com  Questionable  \n",
      "3622    thepoliticalinsider.com  Questionable  \n",
      "3623    thepoliticalinsider.com  Questionable  \n",
      "...                         ...           ...  \n",
      "617059             redstate.com  Questionable  \n",
      "617060             redstate.com  Questionable  \n",
      "617061             redstate.com  Questionable  \n",
      "617062             redstate.com  Questionable  \n",
      "617063             redstate.com  Questionable  \n",
      "\n",
      "[27323 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "quest = df_coordination[(df_coordination.MBFC_category == 'Questionable') | (df_coordination.MBFC_category == 'Conspiracy')]\n",
    "print(quest)\n"
   ]
  }
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